Award Date

May 2017

Degree Type


Degree Name

Master of Science in Computer Science


Computer Science

First Committee Member

Fatma Nasoz

Second Committee Member

Kazem Taghva

Third Committee Member

Justin Zhan

Fourth Committee Member

Qing Wu

Number of Pages



Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as, etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at There is a possibility of mismatch between review submitted and polarity of rating. For, a customer can submit a strongly positive review but give it a low rating. The objective of this thesis is to develop a web-service application which can be used to tackle this situation.

We will perform Sentiment Analysis using Deep Learning on product review data. Product reviews will be converted to vectors using “PARAGRAPH VECTOR” which will later be used to train a Recurrent Neural Network with Gated Recurrent Unit. Our model will incorporate both semantic relationship of review text as well as product information. We have also devel- oped an application in Python, that will predict rating score for the submitted review using the trained model. If there is a mismatch between predicted rating score and submitted rating score, a warning/info will be provided.


Deep Learning; Gated Recurrent Unit; Paragraph Vector; Recurrent Neural Network; Sentiment Analysis; Spam rating


Computer Sciences

File Format


Degree Grantor

University of Nevada, Las Vegas




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